Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults

Sensors

Jalal Alizadeh¹  Joseph Classen¹  Christopher Fricke¹  Martin Bogdan² 

[1] Department of Neurology, Leipzig University, 04103 Leipzig, Germany;Department of Neuromorphic Information Processing, Leipzig University, 04009 Leipzig, Germany;

关键词: fall detection;    machine learning;    SVM;    kNN;    random forest;    older adults;   

DOI  :  10.3390/s21217166

来源: DOAJ

【 摘 要 】

Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications.

【 授权许可】

Unknown